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Next, an auxiliary supervision method with a soft label assignment strategy is designed to help LLM-Net learn more accurate defect features. Finally, the issue of inadequate tail data detection is addressed by employing the devised Binary Cross-Entropy Loss Rank Mining method (BCE-LRM) to identify challenging samples. The performance of LLM-Net was evaluated on a homemade dataset of PCB surface soldering defects, and the results show that LLM-Net achieves the best accuracy of mAP@0.5 for the evaluation metric of the COCO dataset, and it has a real-time inference speed of 188 frames per second (FPS).<\/jats:p>","DOI":"10.1007\/s40747-024-01554-5","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T09:02:45Z","timestamp":1721811765000},"page":"7591-7604","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs"],"prefix":"10.1007","volume":"10","author":[{"given":"Liying","family":"Zhu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1259-8030","authenticated-orcid":false,"given":"Sen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Mingfang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Aiping","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Xuangang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"key":"1554_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106359","volume":"123","author":"W Jiang","year":"2023","unstructured":"Jiang W, Li T, Zhang S, Chen W, Yang J (2023) Pcb defects target detection combining multi-scale and attention mechanism. 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